artillery
Hey
artillery | Hey | |
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29 | 38 | |
7,486 | 17,294 | |
1.1% | - | |
9.7 | 0.0 | |
4 days ago | 11 days ago | |
JavaScript | Go | |
Mozilla Public License 2.0 | Apache License 2.0 |
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artillery
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Ask HN: What are you using for load testing?
Usually, I would let organic users be my load test. However, I am working on a project that has an anticipated load on a new-to-my-team stack, so I'm looking into ways to load test.
I've seen tools like k6 (https://k6.io/), Artillery (https://www.artillery.io), and JMeter (https://jmeter.apache.org/).
I've been using Artillery, but it's hard to visualize the results.
What do you use?
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Tracetest + Artillery Launch Week Recap 💥
This week was Tracetest’s first-ever Launch Week. We’ve been working on a major integration with Artillery for the last month and our team is beyond excited to share it with you all!
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Building Llama as a Service (LaaS)
I found a tool for load testing called Artillery. Following this guide I installed Artillery and began research for the test configuration.
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Ruby on Rails load testing habits
This is a great blog post! just taking the opportunity here to comment on this:
> Finally for full scale high fidelity load tests there are relatively few tools out there for browser based load testing.
It exists as of a few months ago and it's fully open source: https://github.com/artilleryio/artillery (I'm the lead dev). You write a Playwright script, then run it in your own AWS account on serverless Fargate and scale it out horizontally as you see fit. Artillery takes care of spinning up and down all of the infra. It will also automatically grab and report Core Web Vitals for you from all those browser sessions, and we just released support for tracing so you can dig into the details of each session if you want to (OpenTelemetry based so works with most vendors- Datadago APM, New Relic etc)
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Rust and Lambda Performance
So not to stress test Momento or AWS' Lambda, I wanted to build a small but stable 10-minute workload that hits the Momento Topic API and then let Momento trigger the FunctionURL to run the Lambda code. I wrote a small Artillery config file that ramps up to 20 users and then sustains that for the duration. Again, the script is simple to trigger the work.
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API Benchmarking with Artillery and Gitpod: Emulating Production for Enterprises
Tool Spotlight: Featuring insights on how Artillery and Gitpod can enhance and streamline the benchmarking process.
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Timing with Curl (2010)
curl is fantastic. There's also HTTPStat which provides a waterfall visualization on top of curl timings: https://github.com/reorx/httpstat
There's also Skytrace (made by yours truly), which provides timing info as a waterfall visualization inspired by HTTPStat + lots more (syntax highlighting for responses, built-in JMESPath support, command-line assertions and checks etc) - https://github.com/artilleryio/artillery/tree/main/packages/...
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Ask HN: What do you use to stress test your web application?
https://www.artillery.io/
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Is there a way to auto-scale when using the cluster module?
I know it's an annoying answer, but it depends on your application. The only true way to know is to test it using a load tester like artillery. Measuring performance is a fundamental part of any optimisation (otherwise how do you know?), so it's a great idea to be using tools like this anyway.
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Comparison between ARM64 and X86_X64 on ECS Fargate (Node.js)
For this test I have used artillery.io with the following configuration:
Hey
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AWS SnapStart - Part 19 Measuring cold starts and deployment time with Java 17 using different Lambda memory settings
The results of the experiment below were based on reproducing approximately 100 cold starts for the duration of our experiment which ran for approximately 1 hour. For it (and all experiments from my previous articles) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman
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Data API for Amazon Aurora Serverless v2 with AWS SDK for Java - Part 5 Basic cold and warm starts measurements
The results of the experiment to retrieve the existing product from the database by its id see GetProductByIdViaAuroraServerlessV2DataApiHandler with Lambda function with 1024 MB memory setting were based on reproducing more than 100 cold and approximately 10.000 warm starts with experiment which ran for approximately 1 hour. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. We won't enable SnapStart on the Lambda function first.
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AWS SnapStart - Part 15 Measuring cold and warm starts with Java 21 using different synchronous HTTP clients
The results of the experiment below were based on reproducing more than 100 cold and approximately 100.000 warm starts with experiment which ran for approximately 1 hour. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. I ran all these experiments for all 3 scenarios using 2 different compilation options in template.yaml each:
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AWS SnapStart - Part 13 Measuring warm starts with Java 21 using different Lambda memory settings
In our experiment we'll re-use the application introduced in part 9 for this. There are basically 2 Lambda functions which both respond to the API Gateway requests and retrieve product by id received from the API Gateway from DynamoDB. One Lambda function GetProductByIdWithPureJava21Lambda can be used with and without SnapStart and the second one GetProductByIdWithPureJava21LambdaAndPriming uses SnapStart and DynamoDB request invocation priming. We'll measure cold and warm starts using the following memory settings in MBs : 256, 512, 768, 1024, 1536 and 2048. I also put the cold starts measured in the part 12 into the tables to see both cold and warm starts in one place. The results of the experiment below were based on reproducing more than 100 cold and approximately 100.000 warm starts for the duration of our experiment which ran for approximately 1 hour. Here is the code for the sample application. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. Abbreviation c is for the cold start and w is for the warm start.
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Diagnósticos usando dotnet-monitor + prometheus + grafana
Por último, podemos executar os testes de carga usando hey.
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Amazon DevOps Guru for the Serverless applications - Part 2 Setting up the Sample Application for the Anomaly Detection
For running our experiments to provoke anomalies we'll use the stress test tool. You can use the tool of your choice (like Gatling, JMeter, Fiddler or Artillery), I personally prefer to use the tool hey as it is easy to use and similar to curl. On Linux this tool can be installed by executing
- Threadpool no aspnet e problemas de performance
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The Uncreative Software Engineer's Compendium to Testing
Hey: is a fast HTTP load testing tool used to test web applications and APIs. It provides a CLI (command-line interface) and supports concurrent requests.
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The TCP receiver only ack the minimum bytes of MSS one by one
The client and server nodes are CentOS7.9/X86_64. If the HTTP POST requests were sent directly to the server with hey -c 1, there are about 0.2% of cases that may timeout. If the HTTP POST requests were sent through an NGINX proxy on the client node, there are about 20% of cases will timeout. I've confirmed that only one backend node has this problem. All other nodes are 100% succeeded even with higher throughput.
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Benchmarking SQLite Performance in Go. Using Go's awesome built-in simple benchmarking tools to investigate SQLite database performance in a couple of different benchmarks, plus a comparison to Postgres.
64 concurrent requests isn't a lot. Modern web apps can typically handle much more than that (depending on what the request does, of course). Try it yourself with a load tester like https://github.com/rakyll/hey against a Go HTTP server, for example the one I've built in https://www.golang.dk/articles/go-and-sqlite-in-the-cloud
What are some alternatives?
k6-examples - Project using K6 and Javascript to create scenarios of Load and Stress Test
Vegeta - HTTP load testing tool and library. It's over 9000!
k6 - A modern load testing tool, using Go and JavaScript - https://k6.io
Apache JMeter - Apache JMeter open-source load testing tool for analyzing and measuring the performance of a variety of services
siege - Siege is an http load tester and benchmarking utility
locust - Write scalable load tests in plain Python 🚗💨
anteon - Anteon (formerly Ddosify) - Effortless Kubernetes Monitoring and Performance Testing. Available on CLI, Self-Hosted, and Cloud
wrk2 - A constant throughput, correct latency recording variant of wrk
grpcurl - Like cURL, but for gRPC: Command-line tool for interacting with gRPC servers
kubernetes - Production-Grade Container Scheduling and Management